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Easy Money — The Greatest Ponzi Scheme Ever and How it is Set to
Destroy the Global Financial System: Vivek Kaul

Sage Publications India Pvt. Ltd.

B 1/I-1, Mohan Cooperative Industrial Area,

Mathura Road, New Delhi-110044

Rs 395

The first and second books in the trilogy traced
the evolution of money through the era of commodities and even people being
treated as money, the rise of gold as money during the World War II, the role
of oil, the Gulf War and the dotcom bubble burst.

Kaul continues the journey in this concluding book
in the trilogy. The beauty of this trilogy is that it makes boring interesting.
The third book is the icing.

After the dotcom bubble burst, one would have
thought that central bankers across the world would have learnt from the
mistakes of Alan Greenspan, the then [2001] Chairman of the US Federal Reserve.
Alas, that was not to be. Greenspan himself continued with the “easy money
policy that had created the dot-com bubble...The low interest rate regime
created conditions that were ideal for a bubble; the only difference this time
around was that real estate replaced stocks as the medium of speculation”. With
these lines, Kaul eases into the post dotcom bubble burst era of the building
up of the sub-prime crisis.

Greenspan’s
warning

The startling revelation is that, just like
Greenspan had warned people of the U.S. of “Irrational Exuberance” much before
the dotcom bubble burst, he did talk about the housing boom way back in 2002
and that it “cannot continue indefinitely”. But as Kaul points out, he chose to
do nothing about it. In fact, the easy money policy continued.

In fact, after the sub-prime crisis, Ben Bernake
(Chairman of the US Federal Reserve) continued his predecessor Greenspan’s
policy of easy money.

The chapter, “Some are more equal than the others”
very interestingly analyses the growing income inequality and ‘borrowing
substituting rising incomes’. The book follows on to explain, very rationally,
the entire cycle of Chinese exports to the U.S., the consumption of goods by
Americans on borrowed money, fuelled by low interest rates, and hence more
demand, more exports by China, investing of the earned dollars by China in U.S.
financial securities! This cycle kept the interest rates in the U.S. low and at
the same time financed the U.S. budget deficit.

While low interest rates were enticing enough to
borrow, the financial institutions came up with innovative ideas like 2/28
option ARMs, liar loans, easy lending terms and general thrust to lend to the
sub-prime borrowers, resulting in the household debt being 140% of the
household income before the sub-prime crisis hit.

How did this happen? Surely someone would have
realised the huge risk to the system with such unsustainable debt levels? Why
didn’t the banks act earlier? What rocked the party? Why did people start
defaulting? In the midst of the crisis, why did the CEO of Citibank get a $95
million severance package when he was quitting? Read the book to know about all
these interesting aspects of the crisis. September 15, 2008, the date Lehman
Brothers filed for bankruptcy is the date the global markets went haywire and
sub-prime crisis became the talk of the global financial system. The
foundations of the crisis were laid decades ago, depending on how far back into
the history one would want to go. However, how much ever far back one would
like to go, there is provision for doing so in this trilogy. Just decide the
period and start reading from then on.

Earlier, in
Japan

What happened in the U.S. in the 2000s had already
happened in Japan in the 1990s. What is needed is that a country not only
learns from the mistakes of its own past, but also learns from the mistakes of
other nations. This would be very relevant for emerging countries as they would
start going through the phases which some of the developed nations have already
gone through.

Lastly, it is worth pointing out that the research
in the book is up-to-date with references to Thomas Piketty’s, “Capital in the
Twenty-First Century”, published in 2014. Events in the global financial system
till the first half of 2014 have been comprehensively covered by the book.

In the introduction, Kaul writes that he feels “the
best books on the current financial crisis are yet to be written. They will
probably start to get published around 2033 (25 years after the current crisis
started)”. But till then, for the next 18 years, this might be the best account
of the what, why and how of the crisis!

The foundations of the crisis were laid
decades ago, depending on how far back into history one would want to go

Friday, May 1, 2015

This article was first published in the IIB Bulletin, Vol 1, Issue 4: Co-Author: Varsha, GS1 India
https://iib.gov.in/IIB/Articles/IIB%20Bulletin%20Q4%202014-15.pdf

Poor data impacts many
areas in the healthcare system. One of the areas that has an impact on
Healthcare Analytics is the way hospitals are identified and stored in the
various databases. In the case of the Insurance Industry, each Insurer has
their own naming convention for Hospitals. For example, Table 1 shows that five
different Insurers can name the same hospital in 5 different ways in their
databases.

Table 1

Database
A

Database
B

Database
C

Database
D

Database
E

ABC
Hospital

The ABC Hospital & Emergency Services

ABC Hospitals Pvt. Ltd

ABC Hospital Group

ABC Group of Hospitals

In the above illustration one
cannot be certain if all the names are referring to the same entity or if they
are all different entities, without painstaking manual intervention. Using the list as it is would not
give a clear picture of the number of claims, average claims, top diseases in a
period in a particular Hospital, total insurance claims paid per Insurer to the
hospital, and many more such statistics.

To overcome this issue it is recommended to identify each entity
(hospital) with a standard and unique number. Think of it as a mailing address:
an identifier for a single location in the world that is globally unique to
that location. No other organization, agency, or affiliate can use it to
identify their locations, but all parties can and should use it to identify
that location.

The Standard adopted
globally to identify a location using a unique and unambiguous number is a GS1
Global Location Numbers (GLNs) based on the GS1 System of Standards. Utilizing
a GLN can help improve data integrity. In turn, it will
help reduce cost and time spent on data cleaning and making it more reliable.

Such a system enables global and unique identification of products
and locations, as well as the continuous, automatic update (i.e.,
synchronizing) of standardized information across all stakeholders. Unique
identification provide the necessary foundation for achieving the best results
when using complementary applications like automatic data capture, e-commerce,
electronic record management, etc.

Insurance Information Bureau of India has undertaken a project to
identify each Hospital in the Health Insurance Providers Network. GS1 India
would allocate a GLN to each hospital, which is a unique, 13-digit number for a
specific location. Implementing GLNs simplifies the exchange of information and
provides the opportunity to manage accurate and authenticated data more
effectively.

The GLN, or the globally unique ID
would not only identify a specific location, but also provide the link to the
information pertaining to it (i.e., a database holding the GLN attributes such
as postal address and GPS co-ordinates of the location, services offered at
that location, key contact person at that location etc.). This is a key
advantage of using a globally unique identifier because all information can be
held and maintained centrally in a database or registry reducing the effort
required to maintain and communicate information between multiple parties on a
national or global basis.

This enables various stakeholders
to simply reference a GLN in communications, as opposed to manually entering
all of the necessary party/location information. Using a GLN to reference
party/location information promotes efficiency, precision and accuracy in
communicating and sharing location information.

Figure 1

Several countries like UK, Australia, Austria, North America
etc. use GLN’s in their procurement processes to enable efficiency and
transparency to deliver better patient care.

The use of GLNs provides a method of identifying locations that are:

·Unique: with a simple structure,
facilitating processing and transmission of data;

·Multi sectoral: the non-significant
characteristic of the GLN allows any location to be identified - regardless of
its activity

·International: location numbers are
unique worldwide.

By identifying hospitals with GLNs enables interoperability with
other GS1 Healthcare Registries in the world, building global visibility of
Indian healthcare facilities, services and capabilities for international
patients

However, the most immediate impact of the Unique Identification
would be on the quality of Analytics. Only when hospitals are properly identified, logged and data generated
on health aspects from them are reliable, can any meaningful analysis be
carried out. A list of unique hospitals will be beneficial to Hospitals,
Insurers, Govt. Agencies and also the Public.

Ministry of Health and Family Welfare is working on
standardizing treatment procedures and costing templates. Efforts are being
made by IRDAI-FICCI to categorize hospitals. Unique Hospitals would complement
all of these projects as well.

A simple illustration may be seen in Table 2 where the outlier
analysis throws out more meaningful results when the hospital is correctly
identified.

Table 2

Cost of treatment for Disease type Cholera

Database A

Database B

Database C

Database D

Database E

ABC Hospital

The ABC Hospital & Emergency Services

ABC Hospitals Pvt. Ltd

ABC Hospital Group

ABC Group of Hospitals

Claim Paid 1

16,016

2,093

33,115

24,299

39,113

Claim Paid 2

16,577

27,929

22,919

19,366

26,343

Claim Paid 3

12,122

23,767

30,916

29,279

26,000

Claim Paid 4

16,134

25,958

31,108

21,147

15,500

Claim Paid 5

10,280

15,981

1,99,400

26,828

25,000

Average claim paid per hospital

14,226

19,146

63,492

24,184

26,391

Overall Average claim paid

29,488

Highlight Outliers where Claim paid or amount
claimed is above/below +/- 50% of the average for the hospital

Database A

Database B

Database C

Database D

Database E

ABC Hospital

The ABC Hospital & Emergency Services

ABC Hospitals Pvt. Ltd

ABC Hospital Group

ABC Group of Hospitals

Claim Paid 1

-

Outlier

Outlier

-

-

Claim Paid 2

-

-

Outlier

-

-

Claim Paid 3

-

-

Outlier

-

-

Claim Paid 4

-

-

Outlier

-

-

Claim Paid 5

-

-

Outlier

-

-

If the Hospital is identified as the same
hospital in all databases, the average claim paid will be Rs 29,488/- across
all 25 claims.

This article was first published in the IIB Bulletin, Vol 1, Issue 4: Co-Author: Vishnu Vardhan, Syed Md. Ismail

https://iib.gov.in/IIB/Articles/IIB%20Bulletin%20Q4%202014-15.pdf

Inference

It may be inferred from the graph and the GLM analysis that
higher the Literacy rate and the GDP per Capita, higher will be the Health
Insurance Premiums in the State. In a research done at the Indian Institute of
Management, Ahmedabad (Bhat & Jain, 2006) it was found that the purchase of
health insurance is related to the awareness and knowledge about insurance and
also the income of the household. Almost a decade later, our findings suggest
the same.